Search NASA⌕ Search

SEARCH · Search NASA

Results for “landform mapping”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗
NASA NTRSDate not supplied

GeoAI Advances in Specific Landform Mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation.

machine learning↗
NASA NTRS1977

Development of remote sensing technology in New Zealand, part 1. Seismotectonic, structural, volcanologic and geomorphic study of New Zealand, part 2. Indigenous forest assessment, part 3. Mapping land use and environmental studies in New Zealand, part 4. New Zealand forest service LANDSAT projects, part 5. Vegetation map and landform map of Aupouri Peninsula, Northland, part 6. Geographical applications of LANDSAT mapping, part 7

The author has identified the following significant results. Inspection of pixels obtained from LANDSAT of New Zealand revealed that not only can ships and their wakes be detected, but that information on the size, state of motion, and direction of movement was inferred by calculating the total number of pixels occupied by the vessel and wake, the orientation of these pixels, and the sum of their radiance values above the background level. Computer enhanced images showing the Waimihia State Forest and much of Kaingaroa State Forest on 22 December 1975 were examined. Most major forest categories were distinguished on LANDSAT imagery. However, the LANDSAT imagery seemed to be most useful for updating and checking existing forest maps, rather than making new maps with many forest categories. Snow studies were performed using two basins: Six Mile Creek and Mt. Robert. The differences in radiance levels indicated that a greater areal snow cover in Six Mile Creek Basin with the effect of lower radiance values from vegetation/snow regions. A comparison of the two visible bands (MSS 4 and 5) demonstrate this difference for the two basins.

Probine, M. C.↗
NASA NTRS2002

Low Altitude AVIRIS Data for Mapping Landform Types on West Ship Island, Mississippi

A chain of barrier islands provides protection against hurricanes and severe storms along the south and southeastern shores of the United States. Barrier island landform types can be spectrally similar and as small as a few meters across, making highly detailed maps difficult to produce. To determine whether high-resolution airborne hyperspectral imagery could provide detailed maps of barrier island landform types, we used low-altitude hyperspectral and multispectral imagery to map surface environments of West Ship Island, Mississippi. We employed 3.4-meter AVIRIS hyperspectral imagery acquired in July 1999 and 0.5-meter ADAR multispectral data acquired in November 1997. The data were co-registered to digital ortho aerial imagery, and the AVIRIS data was scaled to ground reflectance using ATREM software. Unsupervised classification of AVIRIS and ADAR data proceeded using ISODATA clustering techniques. The resulting landform maps were field-checked and compared to aerial photography and digital elevation maps. Preliminary analyses indicated that the AVIRIS classification mapped more landform types, while the ADAR-based map enabled smaller patches to be identified. Used together, these maps provided a means to assess landform distributions of West Ship Island before and after Hurricane Gorges. Classification accuracy is being addressed through photo-interpretation and field surveys of sample areas selected with stratified random sampling.

Spruce, Joseph↗
NASA NTRS2003

Low Altitude AVIRIS Data for Mapping Landform Types on West Ship Island, Mississippi

A chain of barrier islands provides protection against hurricanes and severe storms along the southern and southeastern shores of the Unites States. Barrier island landform types can be spectrally similar and as small as a few meters across, making highly detailed maps difficult to produce. To determine whether high-resolution airborne hyperspectral imagery could provide detailed maps of barrier island landform types, we used low-altitude hyperspectral and multispectral imagery to map surface environments of West Ship Island, Mississippi. We employed 3.4 meter AVIRIS hyperspectral imagery acquired in July 1999 and 0.5 meter ADAR multispectral data acquired in November 1997. The data were co-registered to digital ortho aerial imagery, and the AVIRIS data was scaled to ground reflectance using ATREM software. Unsupervised classification of AVIRIS and ADAR data proceeded using ISODATA clustering techniques. The resulting landform maps were field-checked and compared to aerial photography and digital elevation maps. Preliminary analyses indicated that the AVIRIS classification mapped more landform types, while the ADAR-based map enabled smaller patches to be identified. Used together, these maps provided a means to assess landform distributions of West Ship Island before and after Hurricane Georges. Classification accuracy is being assessed through photo-interpretation and field surveys of sample areas selected with stratified random sampling.

Spruce, Joseph↗
NASA NTRS1975

Applications of Skylab EREP photographs to mapping landforms and environmental geomorphology in the Great Plains and Midwest

The following evaluations of Skylab photographs were undertaken: (1) the 1290 Skylab S190A and S190B photographs of Illinois, Iowa, Kansas, Missouri, Nebraska, and South Dakota were evaluated in detail in terms of coverage, cloud cover, photographic quality, endlap, detectability of roads and stereorelief, and utility for geomorphologic mapping, and (2) the utility of the Skylab photos were tested for interpretive analytic mapping of geomorphologic features over large areas representative of different parts of this region. Photointerpretative maps of analytic geomorphology were obtained for various test areas representative of the varied landscapes in the region. These maps are useful for regional land-use planning, ground-water exploration, and other environmental geomorphologic-geologic applications. Compared with LANDSAT-1 MSS images, Skylab photos afford almost as extensive overviews of large areas but in considerably greater detail, and for many SL photos, moderate stereorelief. However, repetitive multiseasonal, cloud-free coverage by high-quality photos is very limited and many areas have no coverage at all.

Morrison, R. B.↗
NASA NTRS1973

Mapping quaternary landforms and deposits in the Midwest and Great Plains by means of ERTS-1 multispectral imagery

ERTS-1 multispectral images are proving effective for differentiating many kinds of Quaternary surficial deposits and landforms units in Illinois, Iowa, Missouri, Kansas, Nebraska, and South Dakota. Examples of features that have been distinguished are: (1) the more prominent end moraines of the last glaciation; (2) certain possible palimpsests of older moraines mantled by younger deposits; (3) various abandoned river valleys, including suspected ones deeply filled by deposits; (4) river terraces; and (5) some known faults and a few previously unmapped lineaments that may be faults. The ERTS images are being used for systematic mapping of Quaternary landforms and deposits in about 20 potential study areas. Some study areas, already well mapped, provide checks on the reliability of mapping from the images. For other study areas, previously mapped only partly or not at all, our maps will be the first comprehensive, synoptic ones, and should be useful for regional land-use planning and ground-water, engineering-geology, and other environmental applications.

Morrison, R. B.↗
NASA NTRS1974

Applications of Skylab EREP photographs to mapping of landforms and environmental geology in the Great Plains and Midwest

The author has identified the following significant results. The utility of Skylab 2 and 3 S-190A multispectral photos for environmental-geologic/geomorphic applications is being tested by using them to prepare 1:250,000-scale maps of geomorphic features, surficial geology, geologic linear features, and soil associations of large, representative parts of the Great Plains and Midwest. Parts of Nebraska, Iowa, Missouri, and South Dakota were mapped. The maps were prepared primarily by interpretation of the S-190A photos, supplemented by information from topographic, geologic, and soil maps and reports. The color band provides the greatest information on geology, soils, and geomorphology; its resolution also is the best of all the multispectral bands and permits maximum detail of mapping. The color-IR band shows well the differences in soil drainage and moisture, and vegetative types, but has only moderate resolution. The B/W-red band is superior for topographic detail and stream alinements. The B/W-infrared bands best show differences in soil moisture and drainage but have poor resolution, especially those from SL 2. The B/W-green band generally is so low contrast and degraded by haze as to be nearly useless. Where stereoscopic coverage is provided, interpretation and mapping are done most efficiently using a Kern PG-2 stereoplotter.

Morrison, R. B.↗
NASA NTRS1973

Evaluation of ERTS-1 imagery for mapping Quaternary deposits and landforms in the Great Plains and Midwest

The author has identified the following significant results. The main landform associations and larger landforms are readily identifiable on the better images and commonly the gross associations of surficial Quaternary deposits also can be differentiated, primarily by information on landforms and soils. Maps showing the Quaternary geologic-terrain units that can be differentiated from the ERTS-1 images are being prepared for study areas in Illinois, Iowa, Missouri, Kansas, Nebraska, and South Dakota. Preliminary maps at 1:1 million scale are given of two of the study areas, the Peoria and Decatur, Illinois, 1 deg x 2 quadrangles. These maps exemplify the first phase of investigations, which consists of identifying and mapping landform and land use characteristics and geologic-surficial materials directly from ERTS-1 images alone, without input of additional data. These maps shown that commonly the boundaries of geologic-terrain units can be identified more accurately on ERTS-1 images than on topographic maps of 1:250,000 scale. From analysis of drainage patterns, stream-divide relations, and tone and textural variations on the ERTS-1 images, the trends of numerous moraines of Wisconsinan and possibly some of Illinoian age were mapped. In the Peoria study area the trend of a buried valley of the Mississippi River is revealed.

Morrison, R. B.↗
NASA NTRS1980

Applications of radar imagery to arctic and subarctic problems

Radar imagery provides year around data acquisition of areas in the Arctic and the Subarctic. The foremost factor influencing the choice of radar imagery as the major data source was the demand for neotric data. The weather is so adverse in parts of Alaska that radar imagery was the only remote sensing technique which could meet the demand. The major map products derived from radar imagery are landform maps and lineament maps. These maps are used to make environmental assessments of areas and to reconstruct the geomorphic history of certain regions or features. Since radar imagery provides information about geologic structure and geomorphic features, it can be used to determine the relationship which exists between geologic structure and geomorphology. Important geologic information related to surface roughness can be obtained through a dry snow cover. Radar imagery is the only remote sensing technique which can provide information needed about sea ice through a cloud cover and dry snow, during strong wind conditions, and throughout the Arctic night.

Cannon, P. J.↗
NASA NTRS1983

Evaluation of SIR-A space radar for geologic interpretation: United States, Panama, Colombia, and New Guinea

Comparisons between LANDSAT MSS imagery, and aircraft and space radar imagery from different geologic environments in the United States, Panama, Colombia, and New Guinea demonstrate the interdependence of radar system geometry and terrain configuration for optimum retrieval of geologic information. Illustrations suggest that in the case of space radars (SIR-A in particular), the ability to acquire multiple look-angle/look-direction radar images of a given area is more valuable for landform mapping than further improvements in spatial resolution. Radar look-angle is concluded to be one of the most important system parameters of a space radar designed to be used for geologic reconnaissance mapping. The optimum set of system parameters must be determined for imaging different classes of landform features and tailoring the look-angle to local topography.

Macdonald, H.↗
NASA NTRS1985

Investigations of the Mars Cratered Terrain: Smooth Plains Boundary in the Eastern Equatorial Region

As part of an ongoing study to place geologic constraints on, and to interpret the origin of the ancient cratered terrain - smooth plains boundary on Mars, structural features in the eastern hemisphere were mapped, looking at wide-scale variations in aereal percentage of crater vocer and distinctive landforms. Maps and a digitized data set for graben, scarps, ridges and elongate plateaus in the eastern hemisphere were completed for 30 deg N to 30 deg. In the equatorial region, results indicate a marked change from the N oriented ridges north of the boundary to the NW orientations of those in the inter and intracrater plains south of the boundary. The WNW orientation of the boundary itself is matched by similar orientations of graben and scraps in the southern terrain. In order to determine whether outlying plateaus and isolated knobby hills found north of the boundary are formed from material of the ancient cratered terrain, the Viking 2 approach color data were used to discriminate surface materials in the eastern hemisphere. In the southern half of the Amenthes quadrange (MC-14), several correlations exist among surficial spectral reflectance and observed geologic units. Old terrain on the eastern rim of the Isidis basin is much redder than the smooth volcanic plains on either side of the rim, even though the plains unit east of the rim is located several kilometers higher than the plains on the floor of Isidis. Based on statistics derived from the color data, the plains within Isidis were found to differ from those north of the boundary zone.

Maxwell, T. A.↗
NASA NTRS1989

Contrasting landform perception with varied radar illumination geometries and at simulated resolutions of Venera and Magellan

The high sensitivity of imaging radars to slope at moderate to low incidence angles enhances the perception of linear topography on images. It reveals broad spatial patterns that are essential to landform mapping and interpretation. As radar responses are strongly directional, the ability to discriminate linear features on images varies with their orientation. Landforms that appear prominent on images where they are transverse to the illumination may be obscure to indistinguishable on images where they are parallel to it. Landform detection is also influenced by the spatial resolution in radar images. Seasat radar images of the Gran Desierto Dunes complex, Sonora, Mexico; the Appalachian Valley and Ridge Province; and accreted terranes in eastern interior Alaska were processed to simulate both Venera 15 and 16 images (1000 to 3000 km resolution) and image data expected from the Magellan mission (120 to 300 m resolution. The Gran Desierto Dunes are not discernable in the Venera simulation, whereas the higher resolution Magellan simulation shows dominant dune patterns produced from differential erosion of the rocks. The Magellan simulation also shows that fluvial processes have dominated erosion and exposure of the folds.

Ford, J. P.↗
NASA NTRS1974

Satellite geological and geophysical remote sensing of Iceland

The author has identified the following significant results. The seasonal change in size of sediment plumes from the many glacial rivers which discharge into the sea along the south coast gives an indication of seasonal changes in melting rates of glaciers. Changes in area of lakes, particularly glacier-margin lakes can be mapped of most of the area covered by glacial ice in Iceland. Recently deglaciated terrain can be distinguished on MSS color composites. The increase in surface area of the ice-dammed lake, Graenalon, was monitored until the occurrence of a jokulhlaup, after which the surface area of the lake was considerably reduced. The effect of two subglacial jokulhlaups on the overlying ice cover can be seen in the form of collapse features in the surface of Vatnajokull. MSS color composites permit the mapping of five distinct vegetation types; forests, cultivated areas, grasslands, reclaimed areas, and lichen-covered bedrock. Features as small as 100 m can be discerned. The ability to map landforms, vegetation distribution, occurrence of snowcover, glaciers, and geologic structure stereoscopically permits a much greater accurate analysis of these features.

Williams, R. S., Jr.↗
NASA NTRS1973

Evaluation of ERTS-1 imagery for mapping Quaternary deposits and landforms in the Great Plains and Midwest

The author has identified the following significant results. The main landform associations and larger landforms are readily identifiable on the better images and commonly the gross associations of surficial Quaternary deposits also can be differentiated, primarily by information on landforms and soils. Maps showing the Quaternary geologic-terrain units that can be differentiated from the ERTS-1 images are being prepared for 20-odd potential study areas in Illinois, Iowa, Missouri, Kansas, Nebraska, and South Dakota. Among the more distinct features are the major moraines and outwash channels of the last (Wisconsin) glaciation. Analysis of dissection/drainage patterns from the synoptic imagery is proving useful for detecting anomalies that may be caused by stream diversions and moraines of pre-Wisconsin glaciations, by variable loess deposition, by tectonism, and other factors. Numerous abandoned river valleys have been mapped. Trend-lines of several known pre-Wisconsin moraine systems have been identified in Iowa, Nebraska, and Kansas, and also several similar trend-lines, that may indicate previously unknown moraine systems of middle and possibly early Pleistocene age, have been found in Iowa and Missouri. The area inundated by a major flood in southwestern Iowa also has been delineated from ERTS-1 imagery.

Morrison, R. B.↗
NASA NTRS1973

Evaluation of ERTS-1 imagery for mapping Quaternary deposits and landforms in the Great Plains and Midwest

The author has identified the following significant results. The main landform associations and larger landforms are readily identifiable on the better images and commonly the gross associations of surficial Quaternary deposits also can be determined primarily by information on landforms and soils (obtained by analysis of stream dissection and drainage and stream-divide patterns, land use patterns, etc.). Maps showing the Quaternary geologic-terrain units that can be distinguished on the ERTS-1 images are being prepared for study areas in Illinois, Iowa, Missouri, Kansas, Nebraska, and South Dakota. Preliminary maps of 1:1,000,000 scale are included for three of the study areas: the Grand Island and Fremont, Nebraska, and the Davenport, Iowa-Illinois, 1 deg x 2 deg quadrangles. These maps exemplify the first phase of investigations, which consists of identifying and mapping landform and land use characteristics and geologic-surficial materials directly from the ERTS-1 images alone, with no additional information. These maps show that commonly the boundaries of geologic-terrain units can be delineated more accurately on ERTS-1 images than on topographic maps at 1:250,000 scale.

Morrison, R. B.↗